Creation of CPQRA Data Base
2009_2
ndsemester
En Sup Yoon
Introduction
Present an overview of the data in CPQRA
Basic information necessary for a CPQRA of new or existing facility
Material information
Process chemistry
Material toxicity
Process flow diagram and P&ID
Control strategy
Operation and maintenance philosophy
Emergency response considerations
Material interactions
Equipment specification
Operating procedure
Maintenance practice
Various type of data that should be considered
Equipment failure rate
Toxicity
Human error
Materials of construction
Ignition source
Location-specific data for nearby population
Meteorology
External events
Nearby waterway, road, railroad and airports
Historical Incident data
May be used directly to estimate top event frequencies
Most of these data sources address major events or failure such as
Leak of toxic materials
Major fires or explosion
Pipeline leaks and rupture
Transportation accidents
Accidents causing fatalities or serious injuries
Data source can be grouped into three categories
Failure mechanisms and cause
Consequence effects
Frequencies of certain types of incidents
Process and Plant Data
Plant layout and system description
Process chemistry
Including side reactions under normal and abnormal conditions)
Physical and chemical properties of all process materials
Chemical and material of construction interactions
Process flow diagram
Including process description and specific operating
parameters such as flow rates, pressures, temperature and stream compositions)
Process design basis
Including external events)
Process utilities
Cooling, steam, electricity, instrument air and utility back-up systems
Water treatment system
Plant layout and system description(cont.)
Equipment specification
Including material of construction
Equipment detail drawings
P&ID
Including utilities and relief systems
Plant layout drawings
Plant and immediate surroundings including elevations
Firewater and drainage drawings
Material properties
Including in-process intermediates
Control logic
Instrument loopsheets, relay logic diagram
Plant layout and system description(cont.)
Operating instrument
Operating philosophy
Storage inventory levels, operating schedule, staffing, start-up and shutdown, operator training, safety policy
Safety equipment
Fire protection, emergency relief interlock and alarm systems
Historical incident and maintenance records
Maintenance philosophy and program
Ignition sources and data
The risk for flammable material is dependent on
The chance that the material ignites
The ignition energy
The level of confinement of the released cloud
Typical source of ignition
Flares, Boiler, Fired heaters
Static electricity
Electrical motors, vehicle traffic
Hot work : welding and cutting
Lightning
Overhead high voltage lines
Mechanical : sparks, friction, impact, vibration
Chemical reaction
Ignition probability
Typically modeled as a function of two components
The first is the probability the ignition source will be present to ignite the mixture
Presence Factor
The second is the probability that, given the ignite source is present, it is actually ignites the cloud in a given time interval
Strength Factor
Each potential ignition source will have its own unique combination of presence and strength factor
Typical ranges for on site-strength factors for hydrocarbons
Furnaces, boilers, heaters 0.9-1.0
Substations 0.001-0.3
Office building 0.1-0.2
Truck loading/Unloading area 0.1-0.5
Cars 0.2-0.4
Construction fabrication shop 0.1-0.5
Chemical Data
Types of data
Thermodynamic data
Including vapor pressure, boiling point, freezing point, critical temperature and pressure, enthalpies, entropies, specific and latent heats, heat of combustion
Flammability
Flash point and fire point
LFL, UFL
Autoignition temperature
Maximum allowable oxygen content
Minimum ignition energy
Deflagration index for gasses
Burning velocity
Dust explosion data
Maximum rate of pressure rise
Maximum rate in a closed chamber
Layer ignition temperature
Types of data(cont.)
Industrial hygiene and toxicity data
Short-term exposure data such as LD50, LC50, ERPG’s
Protective equipment needed
Miscellaneous
Chemical interaction and reactivity data
Shock sensitivity
Thermal analysis data
Accelerating rate calorimetry(ARC) data
Vent sizing package(VSP) data
Reaction kinetics and thermodynamic models
Source
Flammability data
NFPA 325M(1984), Bulletin 627(Zabetakis, 1965), Fire Protection Handbook(cote, 1986)
Dust explosion data
NFPA 68(1994)
Industrial hygiene and toxicity data sources
Threshold Limit Values for Chemical Substances and Physical Agent(1996)
Emergency Response Planning Guidelines(1994)
Pocket Guide to Chemical Hazards(1994)
Chemical reactivity hazards data
Guidelines for Chemical Reactivity Evaluation and Application to Process Design(1995)
Environmental Data
Population data
Necessary to know the population distribution on and around the site to estimate risk
Generally defined as population density
Source of population data for an area
Census reports
Detailed map
Aerial photographs and site inspection
Typical population density estimates for different categories of occupancy
Urban : 19,000-40,000 people/square mile
Suburban : 5,000-19,000 people/square mile
Scattered housing : 250-5,000 people/square mile
Meteorological Data
Weather condition
Major effect on the way a release spreads
Generally use typical 16 point wind rose
Generally many risk analysis employ at least two weather condition
Stable (2m/s, Stability F)
Average condition (5m/s, Stability D)
Meteorological data source
Weather data can be obtained from the National Oceanic and Atmospheric Administration(NOAA)
Geographic Data
Include local and site maps on a scale adequate to met the CPQRA objective
Aerial photographs, on-site tours and local building characteristics also provide useful information
Topographic Data
Local topography is important in modeling the dispersion of a gas
Consider large obstacles ranging from trees to mountains and valley
Four types of models which can be used to analyze such release scenarios
Standard EPA Gaussian models that are corrected for plume lifting as the plume approaches the hill
Drainage flow model(for dense gas flow down slopes)
Three dimensional models
Puff trajectory models for nonhomogeneous wind field
External Event Data
External events are either man-made(aircraft crashes) or natural(earthquake, Floods)
Design data should be obtained on individual critical items(e.g., vessels, pipes, control building, blast wall) to determine their performance under incident conditions
Equipment Reliability Data
Equipment reliability
Defined as the probability that process equipment will perform its intended function adequately for a
specified exposure period
Three important point about equipment reliability
A probability
A function of the exposure period
A function of the definition of equipment failure
R(t) is reliability as a function of t
F(t) is a cumulative failure distribution as a function of t
t is time
) ( 1
)
( t F t
R = −
Probability(Failure) density function, f(t)
Substituting (5.5.3) into (5.5.1)
Since the total area under the probability density function, f(t), must be unity, so
dt t t dF
f ( )
)
( = F t =
∫
t f t dt0
) ( )
(
∫
−
=
t
dt t f t
R
0
) ( 1
) (
∫
∞=
t
dt t f t
R( ) ( )
Equipment failure rates
Defined for time-dependent and demand dependent exposure periods
Time-dependent equipment failure rate
Demand-dependent equipment failure rate
nD represent the number of demand
failure to
exposed equipment
of pieces of
number
unit time per
failures equipment
of number
(t) = λ
failure to
exposed equipment
of pieces of
number
demand per
failures equipment
of number
) (nD = λ
Time-related failure rates
Often represent as the number of failures per 106 hr
For equipment which is normally functioning
Running pump, temperature or pressure transmitter
Demand-related failure rates
Typically given as the number of failure per 103 demands
For equipment that is normally static but is called upon at random interval
A switch, a standby generator
Failure rate versus frequency
Time-related equipment failure frequency can be defined as the number of failure events that occur, divided by the total elapsed calendar time during which these event occur
Time-related equipment failure rate can be defined as the number of failure events that occur, divided by the total elapsed operating time during which these events occur
Probability of failure rate
Represented by a probability distribution function
Probability distribution function
If the independent variable is time, the probability of time-
related failure at some time-in-service, t, is the probability that the equipment will between t0 and t
This probability approaches 1.0 as the equipment ages (t infinite)
Using eq.(5.5.6) convert failure rates to probabilities of failure
λ(t) is instantaneous failure rate
f(t) is probability density function of t
R(t)is reliability as a function of t
T is time
From eqs.(5.5.1) (5.5.2) and (5.5.3)
R(T) λ(t) = F(t)
dt t R
t t dR
) (
) ) (
( =
λ
Eq.(5.5.10) can be integrated and solved for R(t)
Functional form
−
=
∫
t t dtt R
0
) ( exp
)
( λ
] ), ( [ )
(t t t
R = Φ
λ
t and (t)
of function on
distributi probaility
is t]
(t),
[λ λ
Φ
Constant failure rate model
Appropriately used to define the reliability of components which are subject only to failures occurring at random interval
Generally based on the exponential distribution
Reliability of a component in the time interval(0,t)
e is the base of the natural logarithm(2.71828)
λ is the time constant failure rate(1/mean time between failure)
T is operating time for which we want to know the reliability R of the component
e t
t
R( ) = −λ
Complement of the reliability(failure probability)
Pf is the probability of failure of the component in the time interval(0,t)
t
f t e
P ( ) = 1− −λ
Nonconstant failure rate model
The most commonly model is Weibull-distribution- based models
Used to identify the infant mortality, useful
life(constant failure rate) and wear-out modes of failure
Two parameters in Weibull model
Beta parameter(shape parameter)
Determined the shape of the Weibull curve
When the value of shape parameter is greater than 1, the rate of failure increases with time
Alpha parameter(the scale or characteristic life parameter)
Amount the curve is spread out along the abscissa depend on alpha parameter
Expressed in unit of time, typically hours
Reliability formula associated with the Weibull density function
Formula for estimating failure rate associated with the Weibull density function
β )
e (t/
R(t) = − α *
) 1
)(
/ ( )
(t = β α β t β− Fr
Types and Source of Failure Rate data
Plant specific data
Contain failure rates specific to equipment
Valve or pump in use at a facility by manufacturer, make, model and serial number
Generic data
Less specific and detailed than the data derived from specific equipment failure history
The PERD Guidelines offer an generic data base of equipment failure rate data for use in CPQRA
Key Factors Influencing Equipment Failure Data
Various factors influencing equipment failure rate
Equipment description(type, boundaries, size)
Design standards
Materials of construction
Fabrication technique
Quality control
Installation techniques
Startup practice
Operating strategy
Process medium
Internal environment
External environment
Maintenance strategy
Failure mode
Equipment age
Key factors to characterize equipment failure rate(PERD guidelines(AIChE/CCPS)
Equipment description
Process medium
Failure mode
Causes
severity
Failure modes, causes and severity
Failure rate can be characterized according to the mode, cause and severity of failure
Failure mode
Defines the manner in which a piece of equipment fails to perform its intended function
Failure cause
Describes the condition that cause the faulty performance
Failure severity
Express the extent to which equipment performance is impaired for a given failure mode and cause
Equipment age
The variation of equipment failure rate with time-in- service can be divided into three regions
Burn-in
Often called “infant mortality”. The equipment failure rate is high.
Such failure is usually due to factors such as defective manufacturer or incorrect installation
Normal operation
The equipment failure rate declines during normal operation until a constant rate is reached
Wear-out
The equipment failure rate rises again as deterioration sets in, often described as wear-out failure
Failure rate adjustment factors
λ is adjusted equipment failure rate
λg is generic equipment failure rate
fi is adjustment factor i (i=1 to n)
n is number of adjustment factors that apply
∏
== n
i
i g
A λ f
λ
1
Collection and processing of raw plant data
Raw failure data collection and processing procedure
Collect and review raw plant data
Classify and sort raw data into taxonomy data cells
Divide and count demand-related and time-related failures
Estimate exposure periods
Screen for zero failures
Screen for trends by failure mode
Calculate failure rates
Determine confidence limits
Reduce the failure rate data set
Preparation of the CPQRA equipment failure rate data set
Procedure for creating the equipment failure rate data segment of a CPQRA analysis data base
Define equipment in CPQRA
Define taxonomy data cells
Determine data accuracy requirements
Identify failure rate models
Collect available data
Construct initial data set
Screen initial data set
Aggregate and smooth available data
Determine sensitivity and uncertainty in the finalized data set
Human reliability data
Factors can affect the reliability
Types of task
Environmental conditions
System element and characteristics
Types of system
Quality of human engineering of control and displays
Motivation
Level or perceived psychological stress
Skill and training
Presence and quality of written instructions and methods used
Coupling of human actions
Personal redundancy